1. Field of the Invention
The present invention pertains to image enhancement. More particularly, this invention relates to an apparatus and a method for determining the appropriate amount of sharpening for an image.
2. Description of the Related Art
As is known, image sharpening improves the appearance of a digital image. Using this technique, an image can be made to appear sharper by increasing the high frequency components of the image to emphasize edges and textures of the image. There are generally two different approaches for image sharpening, which are typically referred to as global sharpening and adaptive sharpening. The global sharpening approach typically applies the same amount of sharpening over the entire image, usually using a linear or nonlinear filter. The adaptive sharpening approach typically applies different amounts of sharpening to different image regions of the image. The amount of sharpening for an image region is based on the image properties in that region.
There have been a number of known adaptive sharpening methods. Some choose the sharpening filter from a set of directional sharpening filters on a pixel by pixel or region by region basis in accordance with local image data characteristics. Others adjust the sharpening filter adaptively based on the output of an edge detector that looks at local spatial features in the image. Still others propose to apply the singular value decomposition to blocks of image data and modify the singular values to improve the image sharpness. The adaptive sharpening approach is typically more complex than the global sharpening approach. This is due to the fact that the adaptive sharpening adaptively adjusts the amount of sharpening across an image.
A typical implementation of the global sharpening approach first produces a blurred version of the original image to be sharpened. The blurred image is then subtracted from the original unsharpened image to isolate the high frequency components of the original image. These high frequency components of the original image are then weighted and added back to the original image to produce a sharpened image S. The sharpened image S can therefore be written as a function of the original image I and a blurred image B according to the following equation EQU S=I+.lambda.(I-B)
wherein .lambda. is a scalar parameter.
The above sharpening operation is typically implemented by a linear filter, such as an unsharp masking filter. The scalar parameter .lambda. is used in the filter to weight the high frequency components. As can be seen from the equation, the greater value the scalar parameter .lambda. has, the sharper the sharpened image becomes.
The determination of the scalar parameter .lambda. is key to the global image sharpening. If the scalar parameter .lambda. is exceesively high, the image may be over-sharpened. When this occurs, the sharpened image becomes a noisy image having many sharp edges. If the scalar parameter .lambda. is undesirably low, the image may be under-sharpened. When this occurs, the sharpened image is still a blurred image that requires further sharpening.
Prior proposals have been made to determine the appropriate value of the scalar parameter .lambda. in order to determine the appropriate amount of sharpening based either on aspects of the original image or the desired use of the image. One such proposal selects the parameter value based on the local contrast values of the image pixels such that the maximum local contrast value of the sharpened image meets a predetermined target and all other local contrast values to an amount proportional thereto.
Disadvantages are, however, associated with such prior art technique. One disadvantage is that it requires non linear calculation at every pixel location. This typically involves a large amount of non standard computation which requires dedicated hardware. Another disadvantage is that it is sensitive to noise. This is because the technique increases the maximum local contrast to a predetermined target value.